The Microsoft Research-University of Trento Center for Computational and Systems Biology, Piazza Manifattura 1, 38068, Rovereto, TN, Italy.
Brief Funct Genomics. 2012 Nov;11(6):497-504. doi: 10.1093/bfgp/els035. Epub 2012 Aug 20.
In order to better understand several cellular processes, it is helpful to study how various components make up the system. This systems perspective is supported by several modelling tools including network analysis. Networks of protein-protein interactions (PPI networks) offer a way to depict, visualize and quantify the functioning and relative importance of particular proteins in cell function. The toolkit of network analysis ranges from the local indices describing individual proteins (as network nodes) to global indicators of system architecture, describing the total interaction system (as the whole network). We briefly introduce some of these network indices and present a case study where the connectedness and potential functional relationships between certain disease proteins are inferred. We argue that network analysis can be used, in general, to improve databases, to infer novel functions, to quantify positional importance and to support predictions in pathogenesis studies. The systems perspective and network analysis can be of particular importance in studying diseases with complex molecular processes.
为了更好地理解几个细胞过程，研究各种组件如何构成系统是有帮助的。这种系统观点得到了包括网络分析在内的几种建模工具的支持。蛋白质-蛋白质相互作用（PPI）网络为描绘、可视化和量化特定蛋白质在细胞功能中的作用和相对重要性提供了一种方法。网络分析的工具包从描述单个蛋白质（作为网络节点）的局部指标到描述整个相互作用系统（作为整个网络）的系统架构的全局指标。我们简要介绍了其中的一些网络指标，并展示了一个案例研究，推断出某些疾病蛋白之间的连通性和潜在功能关系。我们认为，网络分析通常可用于改进数据库、推断新功能、量化位置重要性以及支持发病机制研究中的预测。在研究具有复杂分子过程的疾病时，系统观点和网络分析可能特别重要。
